The 32-bit CaterpillarSSA program performs extended analysis, forecasting and change-point detection for one-dimensional time series and analysis/forecast of multi-dimensional time series. Macros tools, which serve to remember sequences of program procedures and to perform them automatically, are added (see macros description macros.rtf 470kb or 44kb). The program works under Windows 9x/NT/2000/Me/XP/Vista/W7. You can download evaluation version and try it for 30 days. All the examples of the book ”Analysis of time series structure: SSA and related techniques” are obtained by means of the program. Therefore, this book can be considered as an additional help to the program.

References in zbMATH (referenced in 62 articles )

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  1. Alshammri, Fayed; Pan, Jiazhu: Moving dynamic principal component analysis for non-stationary multivariate time series (2021)
  2. Benth, Fred Espen; Lavagnini, Silvia: Correlators of polynomial processes (2021)
  3. Kalantari, Mahdi: Forecasting COVID-19 pandemic using optimal singular spectrum analysis (2021)
  4. Wang, Haifeng; Cai, Jian-Feng; Wang, Tianming; Wei, Ke: Fast Cadzow’s algorithm and a gradient variant (2021)
  5. Zvonarev, N. K.: Search for weights in the problem of finite-rank signal estimation in the presence of random noise (2021)
  6. Usmanova, K. R.; Zhuravlev, Yu. I.; Rudakov, K. V.; Strijov, V. V.: Approximation of quasiperiodic signal phase trajectory using directional regression (2020)
  7. Khan, Atikur R.; Hassani, Hossein: Dependence measures for model selection in singular spectrum analysis (2019)
  8. Ghodsi, Mansi; Hassani, Hossein; Rahmani, Donya; Silva, Emmanuel Sirimal: Vector and recurrent singular spectrum analysis: which is better at forecasting? (2018)
  9. Kume, Kenji; Nose-Togawa, Naoko: An adaptive orthogonal SSA decomposition algorithm for a time series (2018)
  10. Lahmiri, Salim: Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression (2018)
  11. Noonan, Jack; Zhigljavsky, Anatoly: Approximations of the boundary crossing probabilities for the maximum of moving weighted sums (2018)
  12. Rodrigues, Paulo Canas; Mahmoudvand, Rahim: The benefits of multivariate singular spectrum analysis over the univariate version (2018)
  13. Rodrigues, Paulo Canas; Tuy, Pétala G. S. E.; Mahmoudvand, Rahim: Randomized singular spectrum analysis for long time series (2018)
  14. Kouchaki, Samaneh; Sanei, Saeid: Tensor factorisation for narrowband single channel source decomposition (2017)
  15. Launonen, Ilkka; Holmström, Lasse: Multivariate posterior singular spectrum analysis (2017)
  16. Pasanen, Leena; Laukkanen-Nevala, Päivi; Launonen, Ilkka; Prusov, Sergey; Holmström, Lasse; Niemelä, Eero; Erkinaro, Jaakko: Extraction of sea temperature in the Barents Sea by a scale space multiresolution method -- prospects for Atlantic salmon (2017)
  17. Viljoen, Helena: A comparison of stepwise common singular spectrum analysis and horizontal multi-channel singular spectrum analysis (2017)
  18. Alharbi, Nader; Hassani, Hossein: A new approach for selecting the number of the eigenvalues in singular spectrum analysis (2016)
  19. Golyandina, N. E.; Lomtev, M. A.: Improvement of separability of time series in singular spectrum analysis using the method of independent component analysis (2016)
  20. Hojjati Tavassoli, Zahra; Iranmanesh, Seyed Hossein; Tavassoli Hojjati, Ahmad: Designing a framework to improve time series data of construction projects: application of a simulation model and singular spectrum analysis (2016)

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